import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
from plotly.subplots import make_subplots
from datetime import datetime
coviddf=pd.read_csv(r"C:\Users\DELL\Desktop\rashmi\Practice projecct\covid19 India\data set\covid_india.csv")
coviddf.head(10)
| S. No. | Name of State / UT | Active Cases | Cured/Discharged/Migrated | Deaths | Total Confirmed cases | |
|---|---|---|---|---|---|---|
| 0 | 1 | Andaman and Nicobar | 4 | 7408 | 129 | 7541 |
| 1 | 2 | Andhra Pradesh | 20593 | 1944267 | 13490 | 1978350 |
| 2 | 3 | Arunachal Pradesh | 3032 | 46399 | 237 | 49668 |
| 3 | 4 | Assam | 11719 | 555470 | 5357 | 572546 |
| 4 | 5 | Bihar | 357 | 715119 | 9646 | 725122 |
| 5 | 6 | Chandigarh | 27 | 61132 | 811 | 61970 |
| 6 | 7 | Chhattisgarh | 1780 | 987642 | 13536 | 1002958 |
| 7 | 8 | Dadra and Nagar Haveli and Daman and Diu | 12 | 10636 | 4 | 10652 |
| 8 | 9 | Delhi | 516 | 1411042 | 25065 | 1436623 |
| 9 | 10 | Goa | 992 | 167556 | 3157 | 171705 |
coviddf.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 36 entries, 0 to 35 Data columns (total 6 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 S. No. 36 non-null int64 1 Name of State / UT 36 non-null object 2 Active Cases 36 non-null int64 3 Cured/Discharged/Migrated 36 non-null int64 4 Deaths 36 non-null int64 5 Total Confirmed cases 36 non-null int64 dtypes: int64(5), object(1) memory usage: 1.8+ KB
coviddf.describe()
| S. No. | Active Cases | Cured/Discharged/Migrated | Deaths | Total Confirmed cases | |
|---|---|---|---|---|---|
| count | 36.000000 | 36.000000 | 3.600000e+01 | 36.000000 | 3.600000e+01 |
| mean | 18.500000 | 11448.694444 | 8.626628e+05 | 11871.416667 | 8.859829e+05 |
| std | 10.535654 | 31839.162320 | 1.239847e+06 | 22952.890569 | 1.282199e+06 |
| min | 1.000000 | 4.000000 | 7.408000e+03 | 4.000000 | 7.541000e+03 |
| 25% | 9.750000 | 326.000000 | 6.136675e+04 | 799.750000 | 6.657275e+04 |
| 50% | 18.500000 | 1387.000000 | 4.487250e+05 | 5243.500000 | 4.599410e+05 |
| 75% | 27.250000 | 9166.000000 | 9.721065e+05 | 13501.500000 | 9.892878e+05 |
| max | 36.000000 | 178722.000000 | 6.130137e+06 | 133717.000000 | 6.341759e+06 |
vaccinedf=pd.read_csv(r"C:\Users\DELL\Desktop\rashmi\Practice projecct\covid19 India\data set\cowin_vaccine_data_statewise.csv")
vaccinedf.head(7)
| Updated On | State | Total Individuals Vaccinated | Total Sessions Conducted | Total Sites | First Dose Administered | Second Dose Administered | Male(Individuals Vaccinated) | Female(Individuals Vaccinated) | Transgender(Individuals Vaccinated) | Total Covaxin Administered | Total CoviShield Administered | Total Sputnik V Administered | AEFI | 18-45 years (Age) | 45-60 years (Age) | 60+ years (Age) | Total Doses Administered | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16/01/2021 | India | 48276.0 | 3455.0 | 2957.0 | 48276.0 | 0.0 | 23757.0 | 24517.0 | 2.0 | 579.0 | 47697.0 | NaN | NaN | NaN | NaN | NaN | 48276.0 |
| 1 | 17/01/2021 | India | 58604.0 | 8532.0 | 4954.0 | 58604.0 | 0.0 | 27348.0 | 31252.0 | 4.0 | 635.0 | 57969.0 | NaN | NaN | NaN | NaN | NaN | 58604.0 |
| 2 | 18/01/2021 | India | 99449.0 | 13611.0 | 6583.0 | 99449.0 | 0.0 | 41361.0 | 58083.0 | 5.0 | 1299.0 | 98150.0 | NaN | NaN | NaN | NaN | NaN | 99449.0 |
| 3 | 19/01/2021 | India | 195525.0 | 17855.0 | 7951.0 | 195525.0 | 0.0 | 81901.0 | 113613.0 | 11.0 | 3017.0 | 192508.0 | NaN | NaN | NaN | NaN | NaN | 195525.0 |
| 4 | 20/01/2021 | India | 251280.0 | 25472.0 | 10504.0 | 251280.0 | 0.0 | 98111.0 | 153145.0 | 24.0 | 3946.0 | 247334.0 | NaN | NaN | NaN | NaN | NaN | 251280.0 |
| 5 | 21/01/2021 | India | 365965.0 | 32226.0 | 12600.0 | 365965.0 | 0.0 | 132784.0 | 233143.0 | 38.0 | 5367.0 | 360598.0 | NaN | NaN | NaN | NaN | NaN | 365965.0 |
| 6 | 22/01/2021 | India | 549381.0 | 36988.0 | 14115.0 | 549381.0 | 0.0 | 193899.0 | 355402.0 | 80.0 | 8128.0 | 541253.0 | NaN | NaN | NaN | NaN | NaN | 549381.0 |
coviddf.drop(["S. No."],inplace=True,axis=1)
coviddf.head(5)
| Name of State / UT | Active Cases | Cured/Discharged/Migrated | Deaths | Total Confirmed cases | |
|---|---|---|---|---|---|
| 0 | Andaman and Nicobar | 4 | 7408 | 129 | 7541 |
| 1 | Andhra Pradesh | 20593 | 1944267 | 13490 | 1978350 |
| 2 | Arunachal Pradesh | 3032 | 46399 | 237 | 49668 |
| 3 | Assam | 11719 | 555470 | 5357 | 572546 |
| 4 | Bihar | 357 | 715119 | 9646 | 725122 |
statewise=pd.pivot_table(coviddf,values=['Total Confirmed cases','Cured/Discharged/Migrated','Deaths'],
index='Name of State / UT',aggfunc=max)
statewise['recovery rate']=statewise['Cured/Discharged/Migrated']*100/statewise['Total Confirmed cases']
statewise['mortality rate']=statewise['Deaths']*100/statewise['Total Confirmed cases']
statewise=statewise.sort_values(by="Total Confirmed cases",ascending=False)
statewise.style.background_gradient(cmap="cubehelix")
| Cured/Discharged/Migrated | Deaths | Total Confirmed cases | recovery rate | mortality rate | |
|---|---|---|---|---|---|
| Name of State / UT | |||||
| Maharashtra | 6130137 | 133717 | 6341759 | 96.663039 | 2.108516 |
| Kerala | 3317314 | 17515 | 3513551 | 94.414853 | 0.498499 |
| Karnataka | 2854222 | 36741 | 2915317 | 97.904345 | 1.260275 |
| Tamil Nadu | 2516938 | 34260 | 2571383 | 97.882657 | 1.332357 |
| Andhra Pradesh | 1944267 | 13490 | 1978350 | 98.277201 | 0.681881 |
| Uttar Pradesh | 1685299 | 22771 | 1708689 | 98.631114 | 1.332659 |
| West Bengal | 1503535 | 18202 | 1532379 | 98.117698 | 1.187826 |
| Delhi | 1411042 | 25065 | 1436623 | 98.219366 | 1.744717 |
| Chhattisgarh | 987642 | 13536 | 1002958 | 98.472917 | 1.349608 |
| Odisha | 966928 | 6302 | 984731 | 98.192095 | 0.639972 |
| Rajasthan | 944606 | 8954 | 953793 | 99.036793 | 0.938778 |
| Gujarat | 814720 | 10077 | 825001 | 98.753820 | 1.221453 |
| Madhya Pradesh | 781265 | 10514 | 791937 | 98.652418 | 1.327631 |
| Haryana | 759705 | 9647 | 770042 | 98.657606 | 1.252789 |
| Bihar | 715119 | 9646 | 725122 | 98.620508 | 1.330259 |
| Telengana | 635895 | 3819 | 648388 | 98.073222 | 0.588999 |
| Punjab | 582580 | 16312 | 599365 | 97.199536 | 2.721547 |
| Assam | 555470 | 5357 | 572546 | 97.017532 | 0.935645 |
| Jharkhand | 341980 | 5130 | 347336 | 98.457977 | 1.476956 |
| Uttarakhand | 334456 | 7367 | 342336 | 97.698168 | 2.151979 |
| Jammu and Kashmir | 316496 | 4386 | 322286 | 98.203459 | 1.360903 |
| Himachal Pradesh | 202084 | 3533 | 207344 | 97.463153 | 1.703932 |
| Goa | 167556 | 3157 | 171705 | 97.583646 | 1.838619 |
| Puducherry | 118750 | 1799 | 121421 | 97.800216 | 1.481622 |
| Manipur | 92894 | 1628 | 102889 | 90.285648 | 1.582288 |
| Tripura | 76667 | 766 | 79948 | 95.896082 | 0.958123 |
| Meghalaya | 61445 | 1147 | 68107 | 90.218333 | 1.684115 |
| Chandigarh | 61132 | 811 | 61970 | 98.647733 | 1.308698 |
| Arunachal Pradesh | 46399 | 237 | 49668 | 93.418297 | 0.477168 |
| Mizoram | 30500 | 161 | 43530 | 70.066621 | 0.369860 |
| Nagaland | 26493 | 582 | 28445 | 93.137634 | 2.046054 |
| Sikkim | 24050 | 352 | 27652 | 86.973817 | 1.272964 |
| Ladakh | 20106 | 207 | 20378 | 98.665227 | 1.015801 |
| Dadra and Nagar Haveli and Daman and Diu | 10636 | 4 | 10652 | 99.849793 | 0.037552 |
| Lakshadweep | 10125 | 50 | 10243 | 98.847994 | 0.488138 |
| Andaman and Nicobar | 7408 | 129 | 7541 | 98.236308 | 1.710648 |
### top 10 active cases states
top_10_state_active=coviddf.groupby(by='Name of State / UT').max()['Active Cases'].reset_index()
top_10=top_10_state_active.sort_values(by=['Active Cases'],ascending=False).head(10)
top_10
| Name of State / UT | Active Cases | |
|---|---|---|
| 16 | Kerala | 178722 |
| 20 | Maharashtra | 77905 |
| 15 | Karnataka | 24354 |
| 1 | Andhra Pradesh | 20593 |
| 30 | Tamil Nadu | 20185 |
| 23 | Mizoram | 12869 |
| 3 | Assam | 11719 |
| 25 | Odisha | 11501 |
| 35 | West Bengal | 10642 |
| 31 | Telengana | 8674 |
fig=plt.figure(figsize=(16,9))
sns.barplot(x = "Name of State / UT", y = "Active Cases", data = top_10)
plt.title("top 10 active cases states")
plt.xlabel("states")
plt.ylabel("active cases")
plt.show()
## top 10 state having highest death
top_10_state_death=coviddf.groupby(by='Name of State / UT').max()['Deaths'].reset_index()
top_10_death=top_10_state_death.sort_values(by=['Deaths'],ascending=False).head(10)
top_10_death
| Name of State / UT | Deaths | |
|---|---|---|
| 20 | Maharashtra | 133717 |
| 15 | Karnataka | 36741 |
| 30 | Tamil Nadu | 34260 |
| 8 | Delhi | 25065 |
| 33 | Uttar Pradesh | 22771 |
| 35 | West Bengal | 18202 |
| 16 | Kerala | 17515 |
| 27 | Punjab | 16312 |
| 6 | Chhattisgarh | 13536 |
| 1 | Andhra Pradesh | 13490 |
fig=plt.figure(figsize=(16,9))
sns.barplot(x = "Name of State / UT", y = "Deaths", data = top_10_death)
plt.title("top 10 death cases states")
plt.xlabel("states")
plt.ylabel("death")
plt.show()
vaccinedf.head(5)
| Updated On | State | Total Individuals Vaccinated | Total Sessions Conducted | Total Sites | First Dose Administered | Second Dose Administered | Male(Individuals Vaccinated) | Female(Individuals Vaccinated) | Transgender(Individuals Vaccinated) | Total Covaxin Administered | Total CoviShield Administered | Total Sputnik V Administered | AEFI | 18-45 years (Age) | 45-60 years (Age) | 60+ years (Age) | Total Doses Administered | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16/01/2021 | India | 48276.0 | 3455.0 | 2957.0 | 48276.0 | 0.0 | 23757.0 | 24517.0 | 2.0 | 579.0 | 47697.0 | NaN | NaN | NaN | NaN | NaN | 48276.0 |
| 1 | 17/01/2021 | India | 58604.0 | 8532.0 | 4954.0 | 58604.0 | 0.0 | 27348.0 | 31252.0 | 4.0 | 635.0 | 57969.0 | NaN | NaN | NaN | NaN | NaN | 58604.0 |
| 2 | 18/01/2021 | India | 99449.0 | 13611.0 | 6583.0 | 99449.0 | 0.0 | 41361.0 | 58083.0 | 5.0 | 1299.0 | 98150.0 | NaN | NaN | NaN | NaN | NaN | 99449.0 |
| 3 | 19/01/2021 | India | 195525.0 | 17855.0 | 7951.0 | 195525.0 | 0.0 | 81901.0 | 113613.0 | 11.0 | 3017.0 | 192508.0 | NaN | NaN | NaN | NaN | NaN | 195525.0 |
| 4 | 20/01/2021 | India | 251280.0 | 25472.0 | 10504.0 | 251280.0 | 0.0 | 98111.0 | 153145.0 | 24.0 | 3946.0 | 247334.0 | NaN | NaN | NaN | NaN | NaN | 251280.0 |
vaccinedf.rename(columns={'Updated On':'vaccine_date'},inplace=True)
vaccinedf.head(5)
| vaccine_date | State | Total Individuals Vaccinated | Total Sessions Conducted | Total Sites | First Dose Administered | Second Dose Administered | Male(Individuals Vaccinated) | Female(Individuals Vaccinated) | Transgender(Individuals Vaccinated) | Total Covaxin Administered | Total CoviShield Administered | Total Sputnik V Administered | AEFI | 18-45 years (Age) | 45-60 years (Age) | 60+ years (Age) | Total Doses Administered | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16/01/2021 | India | 48276.0 | 3455.0 | 2957.0 | 48276.0 | 0.0 | 23757.0 | 24517.0 | 2.0 | 579.0 | 47697.0 | NaN | NaN | NaN | NaN | NaN | 48276.0 |
| 1 | 17/01/2021 | India | 58604.0 | 8532.0 | 4954.0 | 58604.0 | 0.0 | 27348.0 | 31252.0 | 4.0 | 635.0 | 57969.0 | NaN | NaN | NaN | NaN | NaN | 58604.0 |
| 2 | 18/01/2021 | India | 99449.0 | 13611.0 | 6583.0 | 99449.0 | 0.0 | 41361.0 | 58083.0 | 5.0 | 1299.0 | 98150.0 | NaN | NaN | NaN | NaN | NaN | 99449.0 |
| 3 | 19/01/2021 | India | 195525.0 | 17855.0 | 7951.0 | 195525.0 | 0.0 | 81901.0 | 113613.0 | 11.0 | 3017.0 | 192508.0 | NaN | NaN | NaN | NaN | NaN | 195525.0 |
| 4 | 20/01/2021 | India | 251280.0 | 25472.0 | 10504.0 | 251280.0 | 0.0 | 98111.0 | 153145.0 | 24.0 | 3946.0 | 247334.0 | NaN | NaN | NaN | NaN | NaN | 251280.0 |
vaccinedf.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 5365 entries, 0 to 5364 Data columns (total 18 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 vaccine_date 5365 non-null object 1 State 5365 non-null object 2 Total Individuals Vaccinated 5360 non-null float64 3 Total Sessions Conducted 5360 non-null float64 4 Total Sites 5360 non-null float64 5 First Dose Administered 5360 non-null float64 6 Second Dose Administered 5360 non-null float64 7 Male(Individuals Vaccinated) 5360 non-null float64 8 Female(Individuals Vaccinated) 5360 non-null float64 9 Transgender(Individuals Vaccinated) 5360 non-null float64 10 Total Covaxin Administered 5360 non-null float64 11 Total CoviShield Administered 5360 non-null float64 12 Total Sputnik V Administered 734 non-null float64 13 AEFI 3179 non-null float64 14 18-45 years (Age) 3174 non-null float64 15 45-60 years (Age) 3175 non-null float64 16 60+ years (Age) 3175 non-null float64 17 Total Doses Administered 5364 non-null float64 dtypes: float64(16), object(2) memory usage: 754.6+ KB
vaccinedf.isnull().sum()
vaccine_date 0 State 0 Total Individuals Vaccinated 5 Total Sessions Conducted 5 Total Sites 5 First Dose Administered 5 Second Dose Administered 5 Male(Individuals Vaccinated) 5 Female(Individuals Vaccinated) 5 Transgender(Individuals Vaccinated) 5 Total Covaxin Administered 5 Total CoviShield Administered 5 Total Sputnik V Administered 4631 AEFI 2186 18-45 years (Age) 2191 45-60 years (Age) 2190 60+ years (Age) 2190 Total Doses Administered 1 dtype: int64
vaccine=vaccinedf.drop(columns=['Total Sputnik V Administered','AEFI','18-45 years (Age)',
'45-60 years (Age)','60+ years (Age)'],axis=1)
vaccine.head(5)
| vaccine_date | State | Total Individuals Vaccinated | Total Sessions Conducted | Total Sites | First Dose Administered | Second Dose Administered | Male(Individuals Vaccinated) | Female(Individuals Vaccinated) | Transgender(Individuals Vaccinated) | Total Covaxin Administered | Total CoviShield Administered | Total Doses Administered | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16/01/2021 | India | 48276.0 | 3455.0 | 2957.0 | 48276.0 | 0.0 | 23757.0 | 24517.0 | 2.0 | 579.0 | 47697.0 | 48276.0 |
| 1 | 17/01/2021 | India | 58604.0 | 8532.0 | 4954.0 | 58604.0 | 0.0 | 27348.0 | 31252.0 | 4.0 | 635.0 | 57969.0 | 58604.0 |
| 2 | 18/01/2021 | India | 99449.0 | 13611.0 | 6583.0 | 99449.0 | 0.0 | 41361.0 | 58083.0 | 5.0 | 1299.0 | 98150.0 | 99449.0 |
| 3 | 19/01/2021 | India | 195525.0 | 17855.0 | 7951.0 | 195525.0 | 0.0 | 81901.0 | 113613.0 | 11.0 | 3017.0 | 192508.0 | 195525.0 |
| 4 | 20/01/2021 | India | 251280.0 | 25472.0 | 10504.0 | 251280.0 | 0.0 | 98111.0 | 153145.0 | 24.0 | 3946.0 | 247334.0 | 251280.0 |
#male vs female vaccination
male=vaccine['Male(Individuals Vaccinated)'].sum()
female=vaccine['Female(Individuals Vaccinated)'].sum()
px.pie(names=["male","female"],values=[male,female],title="male and female vaccination")
# remove rows where state is India
vaccine1=vaccinedf[vaccinedf.State!='India']
vaccine1
| vaccine_date | State | Total Individuals Vaccinated | Total Sessions Conducted | Total Sites | First Dose Administered | Second Dose Administered | Male(Individuals Vaccinated) | Female(Individuals Vaccinated) | Transgender(Individuals Vaccinated) | Total Covaxin Administered | Total CoviShield Administered | Total Sputnik V Administered | AEFI | 18-45 years (Age) | 45-60 years (Age) | 60+ years (Age) | Total Doses Administered | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | 16/01/2021 | Andaman and Nicobar Islands | 23.0 | 2.0 | 2.0 | 23.0 | 0.0 | 12.0 | 11.0 | 0.0 | 0.0 | 23.0 | NaN | NaN | NaN | NaN | NaN | 23.0 |
| 146 | 17/01/2021 | Andaman and Nicobar Islands | 23.0 | 2.0 | 2.0 | 23.0 | 0.0 | 12.0 | 11.0 | 0.0 | 0.0 | 23.0 | NaN | NaN | NaN | NaN | NaN | 23.0 |
| 147 | 18/01/2021 | Andaman and Nicobar Islands | 42.0 | 9.0 | 2.0 | 42.0 | 0.0 | 29.0 | 13.0 | 0.0 | 0.0 | 42.0 | NaN | NaN | NaN | NaN | NaN | 42.0 |
| 148 | 19/01/2021 | Andaman and Nicobar Islands | 89.0 | 12.0 | 2.0 | 89.0 | 0.0 | 53.0 | 36.0 | 0.0 | 0.0 | 89.0 | NaN | NaN | NaN | NaN | NaN | 89.0 |
| 149 | 20/01/2021 | Andaman and Nicobar Islands | 124.0 | 16.0 | 3.0 | 124.0 | 0.0 | 67.0 | 57.0 | 0.0 | 0.0 | 124.0 | NaN | NaN | NaN | NaN | NaN | 124.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 5360 | 05/06/2021 | West Bengal | 12090072.0 | 981547.0 | 2517.0 | 12090072.0 | 3941080.0 | 6784722.0 | 5303588.0 | 1762.0 | 1806377.0 | 14224775.0 | 0.0 | 1211.0 | 2999339.0 | 4927157.0 | 4159589.0 | 16031152.0 |
| 5361 | 06/06/2021 | West Bengal | 12206706.0 | 479793.0 | 1016.0 | 12206706.0 | 3943243.0 | 6851075.0 | 5353848.0 | 1783.0 | 1825771.0 | 14324178.0 | 0.0 | 1214.0 | 3058135.0 | 4968447.0 | 4175911.0 | 16149949.0 |
| 5362 | 07/06/2021 | West Bengal | 12492937.0 | 1062959.0 | 2523.0 | 12492937.0 | 3960942.0 | 7014307.0 | 5476794.0 | 1836.0 | 1878776.0 | 14575103.0 | 0.0 | 1223.0 | 3174029.0 | 5087762.0 | 4226545.0 | 16453879.0 |
| 5363 | 08/06/2021 | West Bengal | 12742698.0 | 1026098.0 | 2358.0 | 12742698.0 | 3974349.0 | 7157564.0 | 5583273.0 | 1861.0 | 1931666.0 | 14785235.0 | 146.0 | 1238.0 | 3290866.0 | 5179191.0 | 4267590.0 | 16717047.0 |
| 5364 | 09/06/2021 | West Bengal | 12954543.0 | 887059.0 | 1952.0 | 12954543.0 | 3986376.0 | 7279703.0 | 5672936.0 | 1904.0 | 1985988.0 | 14954421.0 | 510.0 | 1254.0 | 3411008.0 | 5242947.0 | 4294961.0 | 16940919.0 |
5220 rows × 18 columns
vaccine1.rename(columns={'Total Doses Administered':'total'},inplace=True)
vaccine1.head(5)
C:\Users\DELL\anaconda3\lib\site-packages\pandas\core\frame.py:5039: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
| vaccine_date | State | Total Individuals Vaccinated | Total Sessions Conducted | Total Sites | First Dose Administered | Second Dose Administered | Male(Individuals Vaccinated) | Female(Individuals Vaccinated) | Transgender(Individuals Vaccinated) | Total Covaxin Administered | Total CoviShield Administered | Total Sputnik V Administered | AEFI | 18-45 years (Age) | 45-60 years (Age) | 60+ years (Age) | total | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | 16/01/2021 | Andaman and Nicobar Islands | 23.0 | 2.0 | 2.0 | 23.0 | 0.0 | 12.0 | 11.0 | 0.0 | 0.0 | 23.0 | NaN | NaN | NaN | NaN | NaN | 23.0 |
| 146 | 17/01/2021 | Andaman and Nicobar Islands | 23.0 | 2.0 | 2.0 | 23.0 | 0.0 | 12.0 | 11.0 | 0.0 | 0.0 | 23.0 | NaN | NaN | NaN | NaN | NaN | 23.0 |
| 147 | 18/01/2021 | Andaman and Nicobar Islands | 42.0 | 9.0 | 2.0 | 42.0 | 0.0 | 29.0 | 13.0 | 0.0 | 0.0 | 42.0 | NaN | NaN | NaN | NaN | NaN | 42.0 |
| 148 | 19/01/2021 | Andaman and Nicobar Islands | 89.0 | 12.0 | 2.0 | 89.0 | 0.0 | 53.0 | 36.0 | 0.0 | 0.0 | 89.0 | NaN | NaN | NaN | NaN | NaN | 89.0 |
| 149 | 20/01/2021 | Andaman and Nicobar Islands | 124.0 | 16.0 | 3.0 | 124.0 | 0.0 | 67.0 | 57.0 | 0.0 | 0.0 | 124.0 | NaN | NaN | NaN | NaN | NaN | 124.0 |
# most vaccinated state
max_vac=vaccine1.groupby('State')['total'].sum().to_frame('total')
max_vac=max_vac.sort_values('total',ascending=False)[:5]
max_vac
| total | |
|---|---|
| State | |
| Maharashtra | 1.293763e+09 |
| Uttar Pradesh | 1.081672e+09 |
| Rajasthan | 1.055760e+09 |
| Gujarat | 1.033493e+09 |
| West Bengal | 9.009359e+08 |
fig=plt.figure(figsize=(10,5))
sns.barplot(x = max_vac.index, y = max_vac.total, data = max_vac)
plt.title("top 5 vaccinated states")
plt.xlabel("states")
plt.ylabel("vaccination")
plt.show()
# least vaccinated state
least_vac=vaccine1.groupby('State')['total'].sum().to_frame('total')
least_vac=least_vac.sort_values('total',ascending=True)[:5]
least_vac
| total | |
|---|---|
| State | |
| Lakshadweep | 1861077.0 |
| Andaman and Nicobar Islands | 7189806.0 |
| Dadra and Nagar Haveli and Daman and Diu | 7829204.0 |
| Ladakh | 9114076.0 |
| Sikkim | 15267706.0 |
fig=plt.figure(figsize=(10,5))
sns.barplot(x = least_vac.index, y = least_vac.total, data = least_vac)
plt.title("least 5 vaccinated states")
plt.xlabel("states")
plt.ylabel("vaccination")
plt.show()